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指纹识别的应用性研究
其他题名Applied Research on Fingerprint Recognition
韩智
学位类型工学博士
导师刘昌平
2006-06-06
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词生物特征识别 指纹识别 奇异点 配准 指纹分类 融合 Biometric Identification Fingerprint Recognition Singular Point Registration Fingerprint Classification Fusion
摘要指纹识别技术是目前国际公认的应用最广泛,价格最低廉、易用性最高的生物认证技术,具有广阔的应用前景,是一个具有重要理论意义和研究应用价值的模式识别研究课题。本文针对指纹识别技术在实际应用中存在的一些问题,从指纹图像质量评价、指纹图像的配准、指纹分类、指纹特征的提取和匹配、多种指纹匹配方法的融合等多个方面进行了深入的研究。 本文的主要工作包括: 1. 从指纹识别系统实际应用的需要出发,提出了一种指纹图像质量的自动评价方法。该方法从指纹采集的有效区域、指纹按压的位置、图像的灰度变化、方向一致性及脊线纹理清晰程度等多方面综合评价采集到的指纹图像,一方面能够在指纹登录过程中对指纹图像质量不好的情况给用户有效的提示,以便重新采集到更高质量的图像;同时对指纹局部区域的质量等级进行标注,从而提高指纹特征匹配的可靠性。 2. 提出了一种由粗定位和精确定位两阶段组成的指纹图像奇异点的提取方法,并在此基础上提出了一种基于参考点的指纹图像的配准方法,能够有效配准指纹图像,从而提高指纹匹配的可靠性。 3. 提出一种基于指纹灰度统计特征和奇异点信息的指纹分类方法,在NIST-4 数据库的测试图像上分类准确率达到93.23%,在拒绝率为9.8%的情况下,分类准确率达到99.32%,可以有效的提高自动指纹识别系统在大规模指纹数据库下的检索速度。 4. 对基于图像特征的指纹识别方法进行了深入的研究,提出将灰度统计特征、基于灰度共生矩阵的纹理特征,基于LBP 纹理算子的特征用于指纹识别,与最常用的基于Gabor 滤波的指纹识别方法的比较实验表明,所提出的三种方法都具有较好的效果,并且比基于Gabor 滤波的方法的速度要快。 5. 提出了一种多种特征匹配方法融合的指纹识别系统,由粗分类、分类器选择和分类器融合三个过程构成。在分类器融合部分中,用四种基于图像特征的指纹识别方法和一种基于细节点特征的指纹识别方法融合进行指纹识别,能够充分利用不同的识别方法之间的互补性,对指纹识别系统的性能有较大的提高。
其他摘要Fingerprint recognition has become the most widely applied and inexpensive biometric identification technology. In order to solve the problems in the application of fingerprint recognition, we, in this thesis, focus our study on several key models of automatic fingerprint recognition system, such as fingerprint image quality evaluation, fingerprint registration, fingerprint classification, fingerprint feature extraction and matching. The main contributions of this thesis include: 1. According to the requirement in real application of fingerprint identification system, an automatic quality evaluation algorithm of fingerprint images is proposed. The method integrally evaluate the quality of captured fingerprint images in several respects, such as fingerprint foreground area, pressing position of finger, gray-level variation of fingerprint image, orientation consistency and clarity degree of ridge-valley structure. The algorithm can accurately prompt the user for different kinds of bad quality fingerprint images in the process of fingerprint capture, and can also mark the region of bad quality to ensure reliability of fingerprint feature matching 2. A two-stage singular points detection method, which includes coarse locating stage and precise locating stage, is proposed. And then a reference point based fingerprint image registration algorithm is put forward to effectively align the fingerprint images, which can improve the accuracy of fingerprint matching. 3. An automatic fingerprint classification method based on gray-level statistical features and singular point information is proposed. The accuracy of 93.23% with no rejection and 99.32% with 9.8% rejection respectively was achieved on NIST-4 database. Experimental results show that the proposed method, which can greatly reduce the size of the search space and accordingly increase the matching speed of a large-scale fingerprint database, is feasible and reliable for fingerprint classification. 4. An in-depth study was conducted on fingerprint recognition based on image features, including gray-level statistical feature, texture feature based on Gray Level Co-Occurrence Matrix,and histogram feature based on Local Binary Pattern Operator. Experimental results evidence that the 3 proposed methods have higher accuracy and much faster speed than the most famous non-minutiae based method “FingerCode” based on Gabor filter. 5. A fingerprint recognition algorithm is put forward based on the fusion of different fingerprint recognition methods, including process of fingerprint classification, classifier selection and classifier fusion. Four image-based methods and one minutiae-based method are combined for fingerprint recognition. Experimental results indicate that the performance of fusion-based method is better than any method used alone in fusion and can be applied to automatic fingerprint identification system.
馆藏号XWLW994
其他标识符200318014603006
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5939
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
韩智. 指纹识别的应用性研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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